The functional properties of transition metal dichalcogenides (TMDs) may be promoted by the inclusion of other elements. Here, we studied the local stoichiometry of single cobalt promoter atoms in an industrial-style MoS2-based hydrotreating catalyst. Aberration-corrected scanning transmission electron microscopy and electron energy loss spectroscopy show that the Co atoms occupy sites at the (-100) S edge terminations of the graphite-supported MoS2 nanocrystals in the catalyst. Specifically, each Co atom has four neighboring S atoms that are arranged in a reconstructed geometry, which reflects an equilibrium state. The structure agrees with complementary studies of catalysts that were prepared under vastly different conditions and on other supports. In contrast, a small amount of residual Fe in the graphite is found to compete for the S edge sites, so that promotion by Co is strongly sensitive to the purity of the raw materials. The present single-atom-sensitive analytical method therefore offers a guide for advancing preparative methods for promoted TMD nanomaterials.

Intergrowth of two partially miscible phases of BiFeO(3) and BiMnO(3) gives a new class of room-temperature multiferroic phase, Bi(3) Fe(2) Mn(2) O(10+δ) , which has a unique supercell (SC) structure. The SC heterostructures exhibit simultaneously room-temperature ferrimagnetism and remanent polarization. These results open up a new avenue for exploring room-temperature single-phase multiferroic thin films by controlling the phase mixing of two perovskite BiRO(3) (R = Cr, Mn, Fe, Co, Ni) materials.

Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well-defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanisms is still a challenging task. Here, building upon an advanced defect imaging mode that offers high feature clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that performs semantic segmentation of three common crystallographic defects in structural alloys: dislocation lines, precipitates and voids. Results from supervised training on a small set of high-quality defect images of steels show high pixel-wise accuracy across all three types of defects: 91.60 ± 1.77% on dislocations, 93.39 ± 1.00% on precipitates, and 98.85 ± 0.56% on voids. We discuss the sources of uncertainties in CNN prediction and the training data in terms of feature density, representation and homogeneity and their effects on deep learning performance. Further defect quantification using DefectSegNet prediction outperforms human expert average, presenting a promising new workflow for fast and statistically meaningful quantification of materials defects.

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